/**
 * Copyright 2012 Brigham Young University
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package edu.byu.nlp.cluster;

import edu.byu.nlp.data.SparseFeatureVector;
import edu.byu.nlp.util.DoubleArrays;

/**
 * @author rah67
 *
 */
public abstract class AbstractProbabilisticModel implements ProbabilisticModel {

	/** {@inheritDoc} */
	@Override
	public int predict(SparseFeatureVector instance) {
		return DoubleArrays.argMax(unnormalizedLogPOfYGivenX(instance));
	}

	/** {@inheritDoc} */
	@Override
	public double[] pOfYGivenX(SparseFeatureVector instance) {
		double[] logProbs = logPOfYGivenX(instance);
		DoubleArrays.expToSelf(logProbs);
		return logProbs;
	}

	/** {@inheritDoc} */
	@Override
	public double[] logPOfYGivenX(SparseFeatureVector instance) {
		double[] scores = unnormalizedLogPOfYGivenX(instance);
		DoubleArrays.logNormalizeToSelf(scores);
		return scores;
	}

}
